Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 77427

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 84816

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-05T22:31:00+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	163008
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	160871	0
dateAnnounced	0	1.00	10	10	0	312	0
gender	54947	0.66	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	158610	0.03	8	23	0	8	0
notes	88492	0.46	1	270	0	71560	1
mhlwPatientNumber	162559	0.00	1	11	0	434	0
prefecturePatientNumber	50118	0.69	5	20	0	112881	0
prefectureSourceURL	131608	0.19	5	224	0	3455	0
residence	63239	0.61	1	38	0	1429	0
sourceURL	1207	0.99	1	239	0	9250	0
relatedPatients	151052	0.07	2	259	0	7135	0
knownCluster	160474	0.02	3	88	0	235	0
detectedCityTown	134750	0.17	2	22	0	667	0
cityPrefectureNumber	135051	0.17	1	34	0	27948	2
citySourceURL	150775	0.08	7	317	0	3695	0
deceasedDate	160769	0.01	10	10	0	262	0
deceasedReportedDate	161784	0.01	10	62	0	208	0
deathSourceURL	161929	0.01	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 160870, FAL: 2138
charterFlightPassenger	162994	0	1.00	TRU: 14
cruisePassengerDisembarked	162997	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	24.84	24.99	-1	-1	20	40	100	▇▆▃▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	159272
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	159272	0
knownCluster	156788	0.02	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-05	2020-09-25	309
deceasedDate	158893	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	158943	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	52811	0.67	FALSE	2	M: 59531, F: 46930
patientStatus	156761	0.02	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 52896, 20: 28626, 30: 18479, 40: 15555
pcode	0	1.00	FALSE	47	13: 43426, 27: 22198, 14: 13532, 23: 11169
pref	0	1.00	FALSE	47	東京都: 43426, 大阪府: 22198, 神奈川: 13532, 愛知県: 11169
region	0	1.00	FALSE	8	関東地: 77427, 近畿地: 34863, 中部地: 17505, 九州地: 13824
広域圏	14379	0.91	FALSE	8	首都圏: 77840, 近畿圏: 33924, 中部圏: 16027, 九州圏: 9286
通俗的区分	0	1.00	FALSE	11	関東: 77427, 関西: 33924, 東海: 15242, 北海道: 9841
fct_pref	0	1.00	FALSE	47	Tok: 43426, Osa: 22198, Kan: 13532, Aic: 11169

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 159272
charterFlightPassenger	159265	0	1.00	TRU: 7
cruisePassengerDisembarked	159261	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 156788, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.26）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.26）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 175.5709 134.9268 172.0760 156.6822 175.8225 171.8019 161.2497 156.0468
##  [9] 132.1872 151.6921 146.1683 155.2363 152.9074 146.9187
## 
## $北海道地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 161.6540 154.28675
## 47.71429 119.2122 110.89338
## 47.85714 152.6928 142.43193
## 48.00000 136.4658 125.76395
## 48.14286 154.1240 142.63745
## 48.28571 148.4604 136.10419
## 48.42857 136.3079 123.10447
## 48.57143 127.1619 111.87120
## 48.71429 100.5842  83.85448
## 48.85714 116.8175  98.35598
## 49.00000 109.5344  90.14159
## 49.14286 116.6682  96.25142
## 49.28571 112.0503  90.42186
## 49.42857 104.0636  81.37743
## 
## $北海道地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 189.4879 196.8551
## 47.71429 150.6415 158.9603
## 47.85714 191.4592 201.7201
## 48.00000 176.8985 187.6004
## 48.14286 197.5211 209.0076
## 48.28571 195.1434 207.4997
## 48.42857 186.1916 199.3950
## 48.57143 184.9317 200.2224
## 48.71429 163.7903 180.5200
## 48.85714 186.5667 205.0282
## 49.00000 182.8022 202.1950
## 49.14286 193.8044 214.2211
## 49.28571 193.7644 215.3928
## 49.42857 189.7738 212.4599
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 33.90416 31.40680 30.81286 33.98138 37.02830 40.02104 40.43955 39.71311
##  [9] 37.61566 36.20381 35.41340 36.06004 37.13391 38.47968
## 
## $東北地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 26.95641 23.27849
## 47.71429 24.16636 20.33350
## 47.85714 23.43374 19.52747
## 48.00000 26.60220 22.69589
## 48.14286 29.62146 25.70052
## 48.28571 32.56404 28.61655
## 48.42857 32.69161 28.59009
## 48.57143 31.61528 27.32854
## 48.71429 29.06610 24.54024
## 48.85714 27.38004 22.70901
## 49.00000 26.39081 21.61454
## 49.14286 26.94487 22.11959
## 49.28571 27.92763 23.05411
## 49.42857 29.18333 24.26214
## 
## $東北地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 40.85191 44.52983
## 47.71429 38.64724 42.48010
## 47.85714 38.19199 42.09826
## 48.00000 41.36057 45.26687
## 48.14286 44.43514 48.35608
## 48.28571 47.47803 51.42553
## 48.42857 48.18749 52.28900
## 48.57143 47.81094 52.09767
## 48.71429 46.16521 50.69107
## 48.85714 45.02759 49.69861
## 49.00000 44.43599 49.21226
## 49.14286 45.17521 50.00049
## 49.28571 46.34020 51.21371
## 49.42857 47.77603 52.69721
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1]  901.7916  738.8714  866.9102 1045.4355 1124.9106 1097.9029 1145.4652
##  [8]  936.4483  765.9054  896.6131 1083.1438 1170.4294 1147.4745 1194.5809
## 
## $関東地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%      95%
## 47.57143  825.8482 785.6462
## 47.71429  643.3896 592.8446
## 47.85714  764.2372 709.8855
## 48.00000  939.8713 883.9891
## 48.14286 1017.2333 960.2325
## 48.28571  987.2261 928.6373
## 48.42857 1029.9999 968.8763
## 48.57143  807.2230 738.8152
## 48.71429  625.2041 550.7214
## 48.85714  747.5761 668.6806
## 49.00000  928.2072 846.1887
## 49.14286 1010.9153 926.4736
## 49.28571  983.7984 897.1536
## 49.42857 1026.6138 937.6973
## 
## $関東地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143  977.7351 1017.9371
## 47.71429  834.3532  884.8982
## 47.85714  969.5831 1023.9349
## 48.00000 1150.9997 1206.8820
## 48.14286 1232.5878 1289.5887
## 48.28571 1208.5797 1267.1685
## 48.42857 1260.9306 1322.0542
## 48.57143 1065.6737 1134.0814
## 48.71429  906.6067  981.0895
## 48.85714 1045.6501 1124.5455
## 49.00000 1238.0804 1320.0988
## 49.14286 1329.9435 1414.3852
## 49.28571 1311.1505 1397.7954
## 49.42857 1362.5481 1451.4646
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 254.2691 234.1176 275.9855 328.4504 325.7515 335.3365 324.8799 285.2224
##  [9] 249.0280 300.2517 323.6233 338.8485 329.1675 337.5047
## 
## $中部地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 229.9044 217.0066
## 47.71429 203.5291 187.3365
## 47.85714 240.5901 221.8529
## 48.00000 290.1344 269.8511
## 48.14286 284.4287 262.5537
## 48.28571 291.9100 268.9214
## 48.42857 279.0563 254.7988
## 48.57143 233.3897 205.9511
## 48.71429 192.3654 162.3701
## 48.85714 239.8058 207.8077
## 49.00000 259.6397 225.7688
## 49.14286 271.8297 236.3521
## 49.28571 259.1422 222.0730
## 49.42857 264.7991 226.3110
## 
## $中部地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 278.6337 291.5315
## 47.71429 264.7062 280.8988
## 47.85714 311.3810 330.1182
## 48.00000 366.7665 387.0498
## 48.14286 367.0743 388.9493
## 48.28571 378.7629 401.7515
## 48.42857 370.7034 394.9610
## 48.57143 337.0551 364.4936
## 48.71429 305.6906 335.6860
## 48.85714 360.6976 392.6957
## 49.00000 387.6070 421.4779
## 49.14286 405.8674 441.3450
## 49.28571 399.1929 436.2620
## 49.42857 410.2103 448.6984
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 626.7160 472.1639 500.2961 604.8246 653.9883 631.9435 678.2384 629.4755
##  [9] 506.2508 541.4461 629.8912 659.3750 635.9155 661.7275
## 
## $近畿地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 581.8096 558.0376
## 47.71429 418.0818 389.4524
## 47.85714 443.2738 413.0880
## 48.00000 545.0064 513.3405
## 48.14286 591.4992 558.4194
## 48.28571 566.8930 532.4574
## 48.42857 610.7237 574.9836
## 48.57143 552.7639 512.1553
## 48.71429 423.7401 380.0616
## 48.85714 455.0972 409.3869
## 49.00000 539.8676 492.2120
## 49.14286 565.8210 516.2965
## 49.28571 538.9595 487.6341
## 49.42857 561.4849 508.4197
## 
## $近畿地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 671.6224 695.3945
## 47.71429 526.2461 554.8754
## 47.85714 557.3185 587.5043
## 48.00000 664.6428 696.3086
## 48.14286 716.4774 749.5572
## 48.28571 696.9939 731.4295
## 48.42857 745.7530 781.4931
## 48.57143 706.1871 746.7957
## 48.71429 588.7615 632.4400
## 48.85714 627.7950 673.5053
## 49.00000 719.9147 767.5703
## 49.14286 752.9290 802.4535
## 49.28571 732.8715 784.1969
## 49.42857 761.9701 815.0353
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 47.41757 46.65442 46.65442 46.65442 46.65442 46.65442 46.65442 46.65442
##  [9] 46.65442 46.65442 46.65442 46.65442 46.65442 46.65442
## 
## $中国地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 37.63152 32.45109
## 47.71429 36.09290 30.50196
## 47.85714 35.79219 30.04208
## 48.00000 35.49960 29.59459
## 48.14286 35.21448 29.15854
## 48.28571 34.93630 28.73310
## 48.42857 34.66457 28.31752
## 48.57143 34.39886 27.91116
## 48.71429 34.13880 27.51342
## 48.85714 33.88402 27.12378
## 49.00000 33.63424 26.74177
## 49.14286 33.38915 26.36694
## 49.28571 33.14852 25.99892
## 49.42857 32.91209 25.63734
## 
## $中国地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 57.20362 62.38404
## 47.71429 57.21595 62.80689
## 47.85714 57.51665 63.26677
## 48.00000 57.80925 63.71426
## 48.14286 58.09437 64.15031
## 48.28571 58.37255 64.57575
## 48.42857 58.64428 64.99133
## 48.57143 58.90999 65.39769
## 48.71429 59.17005 65.79542
## 48.85714 59.42482 66.18506
## 49.00000 59.67461 66.56708
## 49.14286 59.91969 66.94190
## 49.28571 60.16033 67.30993
## 49.42857 60.39676 67.67151
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 23.16435 22.13645 21.76385 21.62878 21.57982 21.56207 21.55564 21.55331
##  [9] 21.55246 21.55216 21.55205 21.55201 21.55199 21.55199
## 
## $四国地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 19.23484 17.15469
## 47.71429 17.59346 15.18854
## 47.85714 16.91951 14.35508
## 48.00000 16.56774 13.88858
## 48.14286 16.33228 13.55440
## 48.28571 16.14165 13.27225
## 48.42857 15.97017 13.01341
## 48.57143 15.80842 12.76726
## 48.71429 15.65276 12.52965
## 48.85714 15.50172 12.29881
## 49.00000 15.35457 12.07382
## 49.14286 15.21092 11.85414
## 49.28571 15.07047 11.63936
## 49.42857 14.93302 11.42915
## 
## $四国地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 27.09386 29.17402
## 47.71429 26.67945 29.08437
## 47.85714 26.60818 29.17262
## 48.00000 26.68983 29.36898
## 48.14286 26.82736 29.60524
## 48.28571 26.98250 29.85190
## 48.42857 27.14111 30.09787
## 48.57143 27.29820 30.33936
## 48.71429 27.45216 30.57527
## 48.85714 27.60260 30.80551
## 49.00000 27.74952 31.03027
## 49.14286 27.89310 31.24987
## 49.28571 28.03351 31.46462
## 49.42857 28.17095 31.67482
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 129.6247 108.8206 112.4886 118.7596 119.5930 121.5013 118.8254 116.0008
##  [9] 111.5680 115.3201 115.6141 107.7742 112.0291 106.8549
## 
## $九州地方$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 106.37340 94.064894
## 47.71429  80.60633 65.670578
## 47.85714  80.95016 64.254746
## 48.00000  86.40912 69.283811
## 48.14286  85.68232 67.731069
## 48.28571  84.01210 64.166493
## 48.42857  78.07066 56.496390
## 48.57143  69.23778 44.482926
## 48.71429  60.40425 33.319806
## 48.85714  60.31499 31.197027
## 49.00000  57.69603 27.036076
## 49.14286  46.81235 14.541113
## 49.28571  47.62936 13.538218
## 49.42857  39.19174  3.373035
## 
## $九州地方$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 152.8761 165.1846
## 47.71429 137.0350 151.9707
## 47.85714 144.0270 160.7224
## 48.00000 151.1101 168.2354
## 48.14286 153.5037 171.4550
## 48.28571 158.9906 178.8362
## 48.42857 159.5802 181.1544
## 48.57143 162.7638 187.5187
## 48.71429 162.7317 189.8161
## 48.85714 170.3253 199.4432
## 49.00000 173.5321 204.1920
## 49.14286 168.7360 201.0072
## 49.28571 176.4288 210.5199
## 49.42857 174.5180 210.3367

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 175.5709 134.9268 172.0760 156.6822 175.8225 171.8019 161.2497 156.0468
##  [9] 132.1872 151.6921 146.1683 155.2363 152.9074 146.9187
## 
## $北海道$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 161.6540 154.28675
## 47.71429 119.2122 110.89338
## 47.85714 152.6928 142.43193
## 48.00000 136.4658 125.76395
## 48.14286 154.1240 142.63745
## 48.28571 148.4604 136.10419
## 48.42857 136.3079 123.10447
## 48.57143 127.1619 111.87120
## 48.71429 100.5842  83.85448
## 48.85714 116.8175  98.35598
## 49.00000 109.5344  90.14159
## 49.14286 116.6682  96.25142
## 49.28571 112.0503  90.42186
## 49.42857 104.0636  81.37743
## 
## $北海道$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 189.4879 196.8551
## 47.71429 150.6415 158.9603
## 47.85714 191.4592 201.7201
## 48.00000 176.8985 187.6004
## 48.14286 197.5211 209.0076
## 48.28571 195.1434 207.4997
## 48.42857 186.1916 199.3950
## 48.57143 184.9317 200.2224
## 48.71429 163.7903 180.5200
## 48.85714 186.5667 205.0282
## 49.00000 182.8022 202.1950
## 49.14286 193.8044 214.2211
## 49.28571 193.7644 215.3928
## 49.42857 189.7738 212.4599
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 3.583666 5.350824 4.275740 4.929788 4.531885 4.773957 4.626688 4.716282
##  [9] 4.661776 4.694935 4.674762 4.687035 4.679568 4.684111
## 
## $青森県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                   80%        95%
## 47.57143  0.996463279 -0.3731191
## 47.71429  2.333974952  0.7369515
## 47.85714  1.169962884 -0.4741360
## 48.00000  1.588217483 -0.1807030
## 48.14286  1.062835699 -0.7735681
## 48.28571  1.130116070 -0.7988167
## 48.42857  0.847181001 -1.1535690
## 48.57143  0.787861037 -1.2917193
## 48.71429  0.600544174 -1.5493418
## 48.85714  0.498859252 -1.7224093
## 49.00000  0.351740379 -1.9367290
## 49.14286  0.238508705 -2.1163988
## 49.28571  0.110276774 -2.3085601
## 49.42857 -0.003602476 -2.4851280
## 
## $青森県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 6.170869  7.540451
## 47.71429 8.367673  9.964697
## 47.85714 7.381516  9.025615
## 48.00000 8.271358 10.040279
## 48.14286 8.000934  9.837338
## 48.28571 8.417798 10.346730
## 48.42857 8.406195 10.406945
## 48.57143 8.644702 10.724283
## 48.71429 8.723007 10.872893
## 48.85714 8.891012 11.112280
## 49.00000 8.997784 11.286253
## 49.14286 9.135561 11.490469
## 49.28571 9.248860 11.667697
## 49.42857 9.371824 11.853350
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 6.221709 5.129021 6.116786 3.523609 6.931365 3.654277 6.694261 4.262670
##  [9] 5.727978 5.289547 4.743106 6.138826 4.151841 6.420113
## 
## $岩手県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 4.572044 3.6987642
## 47.71429 3.284224 2.3076473
## 47.85714 4.118243 3.0602777
## 48.00000 1.498531 0.4265200
## 48.14286 4.845241 3.7409140
## 48.28571 1.456292 0.2927497
## 48.42857 4.450787 3.2631632
## 48.57143 1.882277 0.6221735
## 48.71429 3.320926 2.0467088
## 48.85714 2.760499 1.4217021
## 49.00000 2.179310 0.8221191
## 49.14286 3.485447 2.0808325
## 49.28571 1.442689 0.0085500
## 49.42857 3.655556 2.1920877
## 
## $岩手県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 7.871374  8.744654
## 47.71429 6.973818  7.950394
## 47.85714 8.115329  9.173294
## 48.00000 5.548686  6.620697
## 48.14286 9.017489 10.121817
## 48.28571 5.852261  7.015803
## 48.42857 8.937735 10.125358
## 48.57143 6.643063  7.903167
## 48.71429 8.135031  9.409248
## 48.85714 7.818595  9.157392
## 49.00000 7.306901  8.664093
## 49.14286 8.792205 10.196819
## 49.28571 6.860994  8.295133
## 49.42857 9.184671 10.648139
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 16.01186 16.78962 15.59728 16.52620 15.80250 16.36631 15.92706 16.26927
##  [9] 16.00267 16.21037 16.04855 16.17462 16.07640 16.15292
## 
## $宮城県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%      95%
## 47.57143 10.785096 8.018216
## 47.71429 11.436834 8.603241
## 47.85714  9.972173 6.994424
## 48.00000 10.874520 7.882703
## 48.14286  9.963293 6.872203
## 48.28571 10.479032 7.362496
## 48.42857  9.895904 6.703201
## 48.57143 10.175077 6.949008
## 48.71429  9.788860 6.499470
## 48.85714  9.924489 6.596946
## 49.00000  9.657446 6.274198
## 49.14286  9.706579 6.282605
## 49.28571  9.512188 6.037302
## 49.42857  9.509586 5.992818
## 
## $宮城県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 21.23862 24.00550
## 47.71429 22.14240 24.97600
## 47.85714 21.22238 24.20013
## 48.00000 22.17787 25.16969
## 48.14286 21.64171 24.73280
## 48.28571 22.25359 25.37012
## 48.42857 21.95822 25.15092
## 48.57143 22.36346 25.58953
## 48.71429 22.21647 25.50586
## 48.85714 22.49625 25.82379
## 49.00000 22.43966 25.82291
## 49.14286 22.64266 26.06663
## 49.28571 22.64062 26.11551
## 49.42857 22.79625 26.31302
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.4042159 0.4545707 0.5045432 0.5162123 0.5230685 0.5251841 0.5261889
##  [8] 0.5265428 0.5266955 0.5267528 0.5267764 0.5267856 0.5267893 0.5267907
## 
## $秋田県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%       95%
## 47.57143 -0.9469965 -1.662285
## 47.71429 -0.9102438 -1.632733
## 47.85714 -0.8739769 -1.603721
## 48.00000 -0.8640243 -1.594677
## 48.14286 -0.8582014 -1.589402
## 48.28571 -0.8566077 -1.588084
## 48.42857 -0.8560307 -1.587734
## 48.57143 -0.8560535 -1.587956
## 48.71429 -0.8562622 -1.588356
## 48.85714 -0.8565593 -1.588841
## 49.00000 -0.8568875 -1.589355
## 49.14286 -0.8572290 -1.589882
## 49.28571 -0.8575754 -1.590414
## 49.42857 -0.8579239 -1.590948
## 
## $秋田県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 1.755428 2.470717
## 47.71429 1.819385 2.541874
## 47.85714 1.883063 2.612808
## 48.00000 1.896449 2.627102
## 48.14286 1.904338 2.635539
## 48.28571 1.906976 2.638452
## 48.42857 1.908409 2.640111
## 48.57143 1.909139 2.641041
## 48.71429 1.909653 2.641747
## 48.85714 1.910065 2.642346
## 49.00000 1.910440 2.642908
## 49.14286 1.910800 2.643453
## 49.28571 1.911154 2.643992
## 49.42857 1.911505 2.644529
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 4.596009 4.494470 4.395175 4.298073 4.203117 4.110259 4.019452 3.930651
##  [9] 3.843812 3.758892 3.675848 3.594638 3.515223 3.437562
## 
## $山形県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 3.309639 2.6286760
## 47.71429 3.145668 2.4316559
## 47.85714 2.989260 2.2450139
## 48.00000 2.839632 2.0675797
## 48.14286 2.696156 1.8984188
## 48.28571 2.558316 1.7367674
## 48.42857 2.425681 1.5819890
## 48.57143 2.297881 1.4335447
## 48.71429 2.174600 1.2909717
## 48.85714 2.055558 1.1538678
## 49.00000 1.940511 1.0218799
## 49.14286 1.829240 0.8946952
## 49.28571 1.721548 0.7720343
## 49.42857 1.617257 0.6536460
## 
## $山形県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 5.882378 6.563341
## 47.71429 5.843272 6.557284
## 47.85714 5.801090 6.545336
## 48.00000 5.756515 6.528567
## 48.14286 5.710078 6.507815
## 48.28571 5.662201 6.483750
## 48.42857 5.613223 6.456915
## 48.57143 5.563421 6.427757
## 48.71429 5.513025 6.396653
## 48.85714 5.462225 6.363916
## 49.00000 5.411184 6.329815
## 49.14286 5.360036 6.294581
## 49.28571 5.308897 6.258411
## 49.42857 5.257866 6.221477
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 5.391774 5.391774 5.391774 5.391774 5.391774 5.391774 5.391774 5.391774
##  [9] 5.391774 5.391774 5.391774 5.391774 5.391774 5.391774
## 
## $福島県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 2.642201 1.1866649
## 47.71429 2.582271 1.0950092
## 47.85714 2.523592 1.0052683
## 48.00000 2.466090 0.9173268
## 48.14286 2.409697 0.8310808
## 48.28571 2.354351 0.7464358
## 48.42857 2.299995 0.6633057
## 48.57143 2.246578 0.5816121
## 48.71429 2.194054 0.5012830
## 48.85714 2.142378 0.4222522
## 49.00000 2.091512 0.3444587
## 49.14286 2.041418 0.2678461
## 49.28571 1.992061 0.1923623
## 49.42857 1.943412 0.1179588
## 
## $福島県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 8.141347  9.596883
## 47.71429 8.201277  9.688539
## 47.85714 8.259956  9.778280
## 48.00000 8.317458  9.866221
## 48.14286 8.373851  9.952467
## 48.28571 8.429197 10.037112
## 48.42857 8.483553 10.120242
## 48.57143 8.536970 10.201936
## 48.71429 8.589494 10.282265
## 48.85714 8.641170 10.361296
## 49.00000 8.692036 10.439089
## 49.14286 8.742130 10.515702
## 49.28571 8.791487 10.591186
## 49.42857 8.840136 10.665589
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 38.63980 48.48626 46.49268 47.88216 50.47489 49.41116 53.10925 46.49226
##  [9] 45.31572 49.01530 48.48530 47.53706 57.21332 44.52457
## 
## $茨城県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 31.89239 28.32053
## 47.71429 41.64810 38.02819
## 47.85714 39.65312 36.03247
## 48.00000 40.84751 37.12359
## 48.14286 43.06226 39.13825
## 48.28571 41.53765 37.36967
## 48.42857 44.75269 40.32900
## 48.57143 37.65449 32.97606
## 48.71429 36.01458 31.09086
## 48.85714 39.26903 34.10966
## 49.00000 38.31088 32.92486
## 49.14286 36.95070 31.34661
## 49.28571 46.22998 40.41575
## 49.42857 33.15788 27.14072
## 
## $茨城県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 45.38721 48.95907
## 47.71429 55.32442 58.94432
## 47.85714 53.33224 56.95289
## 48.00000 54.91681 58.64073
## 48.14286 57.88752 61.81153
## 48.28571 57.28468 61.45266
## 48.42857 61.46581 65.88950
## 48.57143 55.33002 60.00845
## 48.71429 54.61685 59.54057
## 48.85714 58.76158 63.92094
## 49.00000 58.65973 64.04574
## 49.14286 58.12342 63.72750
## 49.28571 68.19667 74.01090
## 49.42857 55.89125 61.90841
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 16.07389 14.21287 18.77099 18.29476 16.50606 16.89976 15.77103 16.39039
##  [9] 16.39039 16.39039 16.39039 16.39039 16.39039 16.39039
## 
## $栃木県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 12.55565 10.693203
## 47.71429 10.59401  8.678295
## 47.85714 15.05423 13.086695
## 48.00000 14.48262 12.464591
## 48.14286 12.60087 10.533582
## 48.28571 12.90367 10.788276
## 48.42857 11.68608  9.523642
## 48.57143 11.98418  9.651681
## 48.71429 11.86285  9.466120
## 48.85714 11.74469  9.285405
## 49.00000 11.62946  9.109173
## 49.14286 11.51695  8.937108
## 49.28571 11.40698  8.768925
## 49.42857 11.29939  8.604375
## 
## $栃木県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 19.59214 21.45459
## 47.71429 17.83173 19.74744
## 47.85714 22.48775 24.45528
## 48.00000 22.10690 24.12492
## 48.14286 20.41126 22.47854
## 48.28571 20.89584 23.01123
## 48.42857 19.85598 22.01842
## 48.57143 20.79659 23.12910
## 48.71429 20.91793 23.31466
## 48.85714 21.03609 23.49537
## 49.00000 21.15132 23.67161
## 49.14286 21.26383 23.84367
## 49.28571 21.37380 24.01185
## 49.42857 21.48139 24.17640
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 29.26664 26.89964 31.78084 31.00584 30.55639 29.56179 30.36275 29.97277
##  [9] 30.07182 30.18006 30.06223 30.14160 30.08813 30.12415
## 
## $群馬県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 24.23718 21.57475
## 47.71429 20.86543 17.67111
## 47.85714 25.46036 22.11449
## 48.00000 24.57288 21.16747
## 48.14286 23.65320 19.99887
## 48.28571 22.47014 18.71605
## 48.42857 22.93265 18.99939
## 48.57143 22.16230 18.02769
## 48.71429 21.87773 17.54004
## 48.85714 21.71465 17.23333
## 49.00000 21.32361 16.69767
## 49.14286 21.12070 16.34533
## 49.28571 20.80492 15.89069
## 49.42857 20.57839 15.52517
## 
## $群馬県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 34.29610 36.95853
## 47.71429 32.93385 36.12817
## 47.85714 38.10133 41.44720
## 48.00000 37.43881 40.84422
## 48.14286 37.45957 41.11390
## 48.28571 36.65345 40.40754
## 48.42857 37.79286 41.72612
## 48.57143 37.78324 41.91786
## 48.71429 38.26591 42.60360
## 48.85714 38.64547 43.12678
## 49.00000 38.80084 43.42678
## 49.14286 39.16251 43.93788
## 49.28571 39.37134 44.28557
## 49.42857 39.66991 44.72314
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 151.7368 126.5225 139.9330 157.2532 162.9242 165.2195 161.7570 162.4121
##  [9] 151.4538 163.0839 168.7111 165.1774 171.3232 171.2820
## 
## $埼玉県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 134.7046 125.68829
## 47.71429 108.4973  98.95538
## 47.85714 121.7479 112.12128
## 48.00000 138.4308 128.46690
## 48.14286 143.4854 133.19518
## 48.28571 145.1833 134.57680
## 48.42857 141.1407 130.22709
## 48.57143 139.2253 126.95096
## 48.71429 127.2764 114.47765
## 48.85714 138.2507 125.10474
## 49.00000 143.0420 129.45356
## 49.14286 138.6988 124.68185
## 49.28571 144.0591 129.62639
## 49.42857 143.2544 128.41757
## 
## $埼玉県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 168.7690 177.7853
## 47.71429 144.5476 154.0895
## 47.85714 158.1180 167.7446
## 48.00000 176.0755 186.0395
## 48.14286 182.3629 192.6532
## 48.28571 185.2557 195.8622
## 48.42857 182.3733 193.2869
## 48.57143 185.5989 197.8733
## 48.71429 175.6313 188.4300
## 48.85714 187.9172 201.0631
## 49.00000 194.3802 207.9686
## 49.14286 191.6560 205.6729
## 49.28571 198.5873 213.0200
## 49.42857 199.3095 214.1464
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 70.91792 67.43573 66.49941 71.62590 71.70569 74.38241 73.67534 70.05774
##  [9] 69.54370 71.46497 71.09019 71.01858 68.93336 68.42467
## 
## $千葉県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 58.39906 51.77198
## 47.71429 53.39098 45.95614
## 47.85714 51.96334 44.26840
## 48.00000 56.61458 48.66806
## 48.14286 56.23371 48.04334
## 48.28571 58.46310 50.03592
## 48.42857 57.32093 48.66342
## 48.57143 52.43615 43.10784
## 48.71429 51.17694 41.45417
## 48.85714 52.53343 42.51169
## 49.00000 51.61024 41.29818
## 49.14286 51.00524 40.41082
## 49.28571 48.40048 37.53103
## 49.42857 47.38507 36.24739
## 
## $千葉県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 83.43678  90.06387
## 47.71429 81.48048  88.91533
## 47.85714 81.03548  88.73042
## 48.00000 86.63722  94.58374
## 48.14286 87.17767  95.36805
## 48.28571 90.30173  98.72890
## 48.42857 90.02976  98.68727
## 48.57143 87.67934  97.00765
## 48.71429 87.91045  97.63322
## 48.85714 90.39650 100.41825
## 49.00000 90.57014 100.88220
## 49.14286 91.03192 101.62635
## 49.28571 89.46624 100.33569
## 49.42857 89.46427 100.60196
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 414.2646 337.6282 394.8333 456.1619 512.7774 479.3274 529.3453 392.4470
##  [9] 318.6010 368.3158 439.3987 499.5012 475.1251 512.4142
## 
## $東京都$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 364.2010 337.6990
## 47.71429 281.1015 251.1780
## 47.85714 334.5801 302.6840
## 48.00000 393.8748 360.9020
## 48.14286 450.0017 416.7703
## 48.28571 415.7111 382.0347
## 48.42857 464.5315 430.2211
## 48.57143 320.6218 282.5997
## 48.71429 242.7651 202.6200
## 48.85714 288.8920 246.8476
## 49.00000 356.7969 313.0701
## 49.14286 414.3809 369.3210
## 49.28571 387.2823 340.7812
## 49.42857 421.7329 373.7292
## 
## $東京都$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 464.3282 490.8302
## 47.71429 394.1550 424.0784
## 47.85714 455.0865 486.9826
## 48.00000 518.4491 551.4219
## 48.14286 575.5531 608.7845
## 48.28571 542.9437 576.6201
## 48.42857 594.1591 628.4695
## 48.57143 464.2723 502.2943
## 48.71429 394.4369 434.5820
## 48.85714 447.7396 489.7841
## 49.00000 522.0004 565.7272
## 49.14286 584.6214 629.6814
## 49.28571 562.9679 609.4691
## 49.42857 603.0955 651.0992
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 163.5227 106.1195 147.1167 192.1567 206.7205 198.5991 194.7501 161.1538
##  [9] 104.8725 147.3437 192.0133 206.3790 198.1826 194.3453
## 
## $神奈川県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 142.35095 131.14329
## 47.71429  82.38459  69.82007
## 47.85714 122.57299 109.58034
## 48.00000 167.57113 154.55633
## 48.14286 181.85776 168.69623
## 48.28571 173.19462 159.74629
## 48.42857 168.60305 154.76165
## 48.57143 132.66762 117.58793
## 48.71429  74.89144  59.02043
## 48.85714 116.18912  99.69687
## 49.00000 159.99714 143.04881
## 49.14286 173.44823 156.01572
## 49.28571 164.33831 146.42223
## 49.42857 159.62424 141.24403
## 
## $神奈川県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 184.6945 195.9022
## 47.71429 129.8545 142.4190
## 47.85714 171.6604 184.6530
## 48.00000 216.7422 229.7570
## 48.14286 231.5832 244.7447
## 48.28571 224.0036 237.4519
## 48.42857 220.8971 234.7385
## 48.57143 189.6400 204.7197
## 48.71429 134.8535 150.7246
## 48.85714 178.4983 194.9906
## 49.00000 224.0294 240.9778
## 49.14286 239.3098 256.7423
## 49.28571 232.0269 249.9429
## 49.42857 229.0663 247.4465
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 3.592432 4.940233 3.099503 4.688423 3.016778 4.752865 3.297386 4.599429
##  [9] 3.313459 4.560827 3.370102 4.505059 3.423258 4.454392
## 
## $新潟県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                  80%        95%
## 47.57143  0.57040294 -1.0293627
## 47.71429  1.78363558  0.1126341
## 47.85714 -0.05877488 -1.7306662
## 48.00000  1.47985656 -0.2186559
## 48.14286 -0.29261878 -2.0445077
## 48.28571  1.39349023 -0.3848552
## 48.42857 -0.15587376 -1.9839192
## 48.57143  1.12075106 -0.7207497
## 48.71429 -0.24287777 -2.1254885
## 48.85714  0.95564114 -0.9528288
## 49.00000 -0.31213808 -2.2613981
## 49.14286  0.78036933 -1.1913622
## 49.28571 -0.37433061 -2.3846524
## 49.42857  0.61439070 -1.4183834
## 
## $新潟県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 6.614461  8.214227
## 47.71429 8.096830  9.767831
## 47.85714 6.257781  7.929672
## 48.00000 7.896989  9.595502
## 48.14286 6.326175  8.078064
## 48.28571 8.112239  9.890585
## 48.42857 6.750646  8.578692
## 48.57143 8.078106  9.919607
## 48.71429 6.869795  8.752406
## 48.85714 8.166012 10.074482
## 49.00000 7.052341  9.001601
## 49.14286 8.229748 10.201480
## 49.28571 7.220846  9.231168
## 49.42857 8.294394 10.327168
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.7016433 0.7325154 0.7618251 0.7896516 0.8160698 0.8411511 0.8649631
##  [8] 0.8875701 0.9090330 0.9294097 0.9487552 0.9671216 0.9845586 1.0011132
## 
## $富山県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 -1.720878 -3.003284
## 47.71429 -1.843793 -3.207608
## 47.85714 -1.945624 -3.378861
## 48.00000 -2.030780 -3.523827
## 48.14286 -2.102453 -3.647426
## 48.28571 -2.163043 -3.753368
## 48.42857 -2.214409 -3.844530
## 48.57143 -2.258024 -3.923201
## 48.71429 -2.295077 -3.991231
## 48.85714 -2.326543 -4.050141
## 49.00000 -2.353228 -4.101193
## 49.14286 -2.375809 -4.145449
## 49.28571 -2.394854 -4.183806
## 49.42857 -2.410848 -4.217031
## 
## $富山県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 3.124165 4.406570
## 47.71429 3.308824 4.672639
## 47.85714 3.469274 4.902511
## 48.00000 3.610083 5.103130
## 48.14286 3.734593 5.279566
## 48.28571 3.845345 5.435670
## 48.42857 3.944335 5.574456
## 48.57143 4.033164 5.698341
## 48.71429 4.113143 5.809297
## 48.85714 4.185363 5.908960
## 49.00000 4.250739 5.998703
## 49.14286 4.310052 6.079692
## 49.28571 4.363971 6.152924
## 49.42857 4.413074 6.219257
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 2.537289 2.537289 2.537289 2.537289 2.537289 2.537289 2.537289 2.537289
##  [9] 2.537289 2.537289 2.537289 2.537289 2.537289 2.537289
## 
## $石川県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 -1.064177 -2.970677
## 47.71429 -1.264228 -3.276629
## 47.85714 -1.454265 -3.567266
## 48.00000 -1.635657 -3.844681
## 48.14286 -1.809486 -4.110530
## 48.28571 -1.976626 -4.366148
## 48.42857 -2.137795 -4.612634
## 48.57143 -2.293589 -4.850901
## 48.71429 -2.444514 -5.081720
## 48.85714 -2.590999 -5.305750
## 49.00000 -2.733414 -5.523555
## 49.14286 -2.872081 -5.735628
## 49.28571 -3.007281 -5.942399
## 49.42857 -3.139262 -6.144247
## 
## $石川県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 6.138754  8.045254
## 47.71429 6.338805  8.351206
## 47.85714 6.528842  8.641843
## 48.00000 6.710234  8.919259
## 48.14286 6.884064  9.185107
## 48.28571 7.051203  9.440726
## 48.42857 7.212372  9.687211
## 48.57143 7.368166  9.925478
## 48.71429 7.519091 10.156298
## 48.85714 7.665576 10.380327
## 49.00000 7.807991 10.598132
## 49.14286 7.946658 10.810205
## 49.28571 8.081858 11.016976
## 49.42857 8.213839 11.218824
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.9690892 0.5970820 0.5465561 0.9425416 0.6627345 0.8037126 0.8979392
##  [8] 0.7777247 0.8993075 0.8894568 0.8648796 0.9285482 0.9042448 0.9145573
## 
## $福井県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%       95%
## 47.57143 -0.9958783 -2.036069
## 47.71429 -1.5326015 -2.659988
## 47.85714 -1.7748735 -3.003764
## 48.00000 -1.6060276 -2.955159
## 48.14286 -1.9298381 -3.302263
## 48.28571 -1.8754266 -3.293677
## 48.42857 -1.8347124 -3.281291
## 48.57143 -1.9746736 -3.431705
## 48.71429 -1.8867877 -3.361657
## 48.85714 -1.9109731 -3.393431
## 49.00000 -1.9453897 -3.433056
## 49.14286 -1.8931906 -3.386929
## 49.28571 -1.9220928 -3.418266
## 49.42857 -1.9163192 -3.414895
## 
## $福井県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 2.934057 3.974248
## 47.71429 2.726765 3.854152
## 47.85714 2.867986 4.096876
## 48.00000 3.491111 4.840242
## 48.14286 3.255307 4.627732
## 48.28571 3.482852 4.901103
## 48.42857 3.630591 5.077169
## 48.57143 3.530123 4.987155
## 48.71429 3.685403 5.160273
## 48.85714 3.689887 5.172345
## 49.00000 3.675149 5.162816
## 49.14286 3.750287 5.244025
## 49.28571 3.730582 5.226755
## 49.42857 3.745434 5.244009
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 14.344961 14.223305 12.175257 11.254452  8.985122 10.125373  8.387818
##  [8]  8.847549  9.585042 10.091104 10.077123 11.376180 10.279666 11.641388
## 
## $山梨県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 12.137137 10.968385
## 47.71429 11.778719 10.484634
## 47.85714  9.461891  8.025521
## 48.00000  8.386465  6.868245
## 48.14286  5.988711  4.402507
## 48.28571  7.028841  5.389636
## 48.42857  5.205809  3.521356
## 48.57143  5.643988  3.948126
## 48.71429  6.340881  4.623526
## 48.85714  6.812763  5.077314
## 49.00000  6.761877  5.006892
## 49.14286  8.024750  6.250610
## 49.28571  6.891767  5.098321
## 49.42857  8.217235  6.404598
## 
## $山梨県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 16.55279 17.72154
## 47.71429 16.66789 17.96198
## 47.85714 14.88862 16.32499
## 48.00000 14.12244 15.64066
## 48.14286 11.98153 13.56774
## 48.28571 13.22190 14.86111
## 48.42857 11.56983 13.25428
## 48.57143 12.05111 13.74697
## 48.71429 12.82920 14.54656
## 48.85714 13.36945 15.10489
## 49.00000 13.39237 15.14735
## 49.14286 14.72761 16.50175
## 49.28571 13.66757 15.46101
## 49.42857 15.06554 16.87818
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 13.05364 13.05364 13.05364 13.05364 13.05364 13.05364 13.05364 13.05364
##  [9] 13.05364 13.05364 13.05364 13.05364 13.05364 13.05364
## 
## $長野県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 9.624117 7.808636
## 47.71429 9.370294 7.420447
## 47.85714 9.132869 7.057337
## 48.00000 8.909023 6.714994
## 48.14286 8.696662 6.390216
## 48.28571 8.494181 6.080548
## 48.42857 8.300318 5.784060
## 48.57143 8.114057 5.499199
## 48.71429 7.934569 5.224696
## 48.85714 7.761165 4.959497
## 49.00000 7.593265 4.702715
## 49.14286 7.430375 4.453598
## 49.28571 7.272073 4.211496
## 49.42857 7.117992 3.975848
## 
## $長野県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 16.48317 18.29865
## 47.71429 16.73699 18.68684
## 47.85714 16.97441 19.04995
## 48.00000 17.19826 19.39229
## 48.14286 17.41062 19.71707
## 48.28571 17.61310 20.02674
## 48.42857 17.80697 20.32322
## 48.57143 17.99323 20.60809
## 48.71429 18.17271 20.88259
## 48.85714 18.34612 21.14779
## 49.00000 18.51402 21.40457
## 49.14286 18.67691 21.65369
## 49.28571 18.83521 21.89579
## 49.42857 18.98929 22.13144
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 27.12631 23.77263 24.55981 28.58459 32.93155 34.56461 32.51031 28.38988
##  [9] 25.16484 25.01382 27.87151 31.59736 33.56918 32.52143
## 
## $岐阜県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 23.19473 21.11349
## 47.71429 19.51298 17.25806
## 47.85714 19.70922 17.14147
## 48.00000 23.49614 20.80248
## 48.14286 27.70453 24.93752
## 48.28571 29.22850 26.40374
## 48.42857 26.99625 24.07729
## 48.57143 22.57316 19.49397
## 48.71429 18.96361 15.68088
## 48.85714 18.47287 15.01031
## 49.00000 21.10960 17.53006
## 49.14286 24.70778 21.06066
## 49.28571 26.58067 22.88118
## 49.42857 25.40217 21.63346
## 
## $岐阜県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 31.05788 33.13913
## 47.71429 28.03228 30.28720
## 47.85714 29.41040 31.97815
## 48.00000 33.67304 36.36670
## 48.14286 38.15856 40.92558
## 48.28571 39.90072 42.72549
## 48.42857 38.02437 40.94334
## 48.57143 34.20661 37.28580
## 48.71429 31.36607 34.64880
## 48.85714 31.55476 35.01732
## 49.00000 34.63342 38.21296
## 49.14286 38.48694 42.13406
## 49.28571 40.55769 44.25718
## 49.42857 39.64070 43.40941
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 43.21493 41.27711 42.55900 45.92314 45.98163 41.05160 37.79021 40.83068
##  [9] 41.94819 41.94819 41.94819 41.94819 41.94819 41.94819
## 
## $静岡県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 35.74124 31.78491
## 47.71429 32.64189 28.07068
## 47.85714 33.53280 28.75461
## 48.00000 36.52220 31.54564
## 48.14286 36.22033 31.05301
## 48.28571 30.94278 25.59149
## 48.42857 27.34543 21.81629
## 48.57143 29.45555 23.43392
## 48.71429 29.98191 23.64735
## 48.85714 29.53840 22.96905
## 49.00000 29.11020 22.31418
## 49.14286 28.69583 21.68045
## 49.28571 28.29403 21.06595
## 49.42857 27.90372 20.46902
## 
## $静岡県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 50.68862 54.64496
## 47.71429 49.91233 54.48355
## 47.85714 51.58521 56.36339
## 48.00000 55.32408 60.30064
## 48.14286 55.74293 60.91025
## 48.28571 51.16042 56.51171
## 48.42857 48.23500 53.76414
## 48.57143 52.20580 58.22743
## 48.71429 53.91447 60.24903
## 48.85714 54.35798 60.92732
## 49.00000 54.78618 61.58220
## 49.14286 55.20055 62.21593
## 49.28571 55.60235 62.83043
## 49.42857 55.99266 63.42736
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 176.3046 185.6926 225.2521 232.4364 204.7300 215.0269 212.8906 212.5155
##  [9] 214.8349 225.1942 218.5822 218.5308 213.9901 219.1351
## 
## $愛知県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 158.5069 149.0854
## 47.71429 164.4440 153.1957
## 47.85714 201.2496 188.5435
## 48.00000 206.9253 193.4206
## 48.14286 178.4857 164.5929
## 48.28571 187.9562 173.6258
## 48.42857 183.9459 168.6235
## 48.57143 177.5044 158.9707
## 48.71429 175.2611 154.3120
## 48.85714 182.7210 160.2370
## 49.00000 173.6351 149.8416
## 49.14286 172.0175 147.3948
## 49.28571 165.5319 139.8796
## 49.42857 168.4594 141.6333
## 
## $愛知県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 194.1023 203.5238
## 47.71429 206.9413 218.1896
## 47.85714 249.2546 261.9608
## 48.00000 257.9474 271.4521
## 48.14286 230.9742 244.8670
## 48.28571 242.0977 256.4280
## 48.42857 241.8354 257.1578
## 48.57143 247.5267 266.0604
## 48.71429 254.4087 275.3579
## 48.85714 267.6675 290.1515
## 49.00000 263.5293 287.3229
## 49.14286 265.0441 289.6667
## 49.28571 262.4483 288.1005
## 49.42857 269.8108 296.6369
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 16.25710 15.36183 13.53606 15.48956 17.76636 16.67593 15.53736 15.77728
##  [9] 15.50504 14.96280 15.40800 16.11946 16.07345 15.02365
## 
## $三重県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%      95%
## 47.57143 11.967267 9.696365
## 47.71429 10.760699 8.325008
## 47.85714  8.643398 6.053380
## 48.00000 10.321790 7.586138
## 48.14286 12.337404 9.463489
## 48.28571 10.997787 7.991962
## 48.42857  9.620520 6.488335
## 48.57143  9.356363 5.957335
## 48.71429  8.784118 5.226278
## 48.85714  7.954711 4.244850
## 49.00000  8.124044 4.268152
## 49.14286  8.569724 4.573134
## 49.28571  8.266966 4.134464
## 49.42857  6.968608 2.704524
## 
## $三重県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 20.54694 22.81784
## 47.71429 19.96296 22.39865
## 47.85714 18.42872 21.01874
## 48.00000 20.65733 23.39298
## 48.14286 23.19531 26.06923
## 48.28571 22.35407 25.35989
## 48.42857 21.45420 24.58638
## 48.57143 22.19819 25.59722
## 48.71429 22.22596 25.78380
## 48.85714 21.97090 25.68076
## 49.00000 22.69195 26.54784
## 49.14286 23.66920 27.66579
## 49.28571 23.87993 28.01243
## 49.42857 23.07870 27.34278
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 5.396198 6.016290 5.847428 6.040454 5.994051 6.054395 6.041949 6.060901
##  [9] 6.057679 6.063659 6.062870 6.064767 6.064592 6.065196
## 
## $滋賀県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%         95%
## 47.57143 1.4720013 -0.60534290
## 47.71429 2.0249937 -0.08787098
## 47.85714 1.4435001 -0.88779855
## 48.00000 1.5499499 -0.82717923
## 48.14286 1.3455246 -1.11525640
## 48.28571 1.3199476 -1.18631749
## 48.42857 1.2051477 -1.35530039
## 48.57143 1.1418860 -1.46208301
## 48.71429 1.0531342 -1.59611151
## 48.85714 0.9802491 -1.71074554
## 49.00000 0.9005734 -1.83218135
## 49.14286 0.8263359 -1.94672194
## 49.28571 0.7507992 -2.06215254
## 49.42857 0.6775721 -2.17446391
## 
## $滋賀県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%      95%
## 47.57143  9.320395 11.39774
## 47.71429 10.007587 12.12045
## 47.85714 10.251356 12.58265
## 48.00000 10.530958 12.90809
## 48.14286 10.642577 13.10336
## 48.28571 10.788842 13.29511
## 48.42857 10.878751 13.43920
## 48.57143 10.979915 13.58388
## 48.71429 11.062223 13.71147
## 48.85714 11.147069 13.83806
## 49.00000 11.225166 13.95792
## 49.14286 11.303198 14.07626
## 49.28571 11.378384 14.19134
## 49.42857 11.452821 14.30486
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 29.30671 22.19731 21.05738 25.03426 27.58605 28.83836 29.74735 26.42469
##  [9] 24.56026 25.84950 25.63886 26.66116 28.92088 30.28725
## 
## $京都府$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%       95%
## 47.57143 22.21839 18.466052
## 47.71429 14.38152 10.244084
## 47.85714 13.11977  8.917862
## 48.00000 17.04856 12.821185
## 48.14286 19.53335 15.270501
## 48.28571 20.67205 16.349074
## 48.42857 21.42536 17.019959
## 48.57143 17.72776 13.123883
## 48.71429 15.60696 10.867373
## 48.85714 16.69689 11.851796
## 49.00000 16.30690 11.366855
## 49.14286 17.15274 12.119292
## 49.28571 19.23379 14.105753
## 49.42857 20.41983 15.196336
## 
## $京都府$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 36.39504 40.14737
## 47.71429 30.01311 34.15054
## 47.85714 28.99498 33.19689
## 48.00000 33.01996 37.24734
## 48.14286 35.63876 39.90161
## 48.28571 37.00466 41.32764
## 48.42857 38.06934 42.47473
## 48.57143 35.12162 39.72550
## 48.71429 33.51356 38.25315
## 48.85714 35.00210 39.84720
## 49.00000 34.97082 39.91086
## 49.14286 36.16958 41.20303
## 49.28571 38.60797 43.73601
## 49.42857 40.15467 45.37816
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 419.4587 321.9848 322.2496 397.0708 385.1581 403.8745 429.0704 417.1058
##  [9] 346.1710 350.9462 414.5562 396.2053 405.9330 417.6500
## 
## $大阪府$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 385.9313 368.1830
## 47.71429 283.5945 263.2719
## 47.85714 282.2395 261.0595
## 48.00000 355.5040 333.4999
## 48.14286 342.0909 319.2925
## 48.28571 359.3574 335.7914
## 48.42857 383.1492 358.8400
## 48.57143 365.0018 337.4196
## 48.71429 290.8078 261.5002
## 48.85714 293.3846 262.9133
## 49.00000 354.8772 323.2850
## 49.14286 334.4814 301.8068
## 49.28571 342.2299 308.5075
## 49.42857 352.0274 317.2889
## 
## $大阪府$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 452.9860 470.7343
## 47.71429 360.3752 380.6978
## 47.85714 362.2597 383.4398
## 48.00000 438.6375 460.6416
## 48.14286 428.2253 451.0237
## 48.28571 448.3916 471.9575
## 48.42857 474.9917 499.3009
## 48.57143 469.2099 496.7921
## 48.71429 401.5343 430.8418
## 48.85714 408.5078 438.9791
## 49.00000 474.2352 505.8274
## 49.14286 457.9291 490.6037
## 49.28571 469.6361 503.3584
## 49.42857 483.2726 518.0111
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 132.40063  91.48577 130.06218 134.21539 175.14731 139.34115 159.11392
##  [8] 142.99265 108.22702 141.00565 144.53466 179.31480 148.89006 165.69111
## 
## $兵庫県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 119.30112 112.36665
## 47.71429  77.57412  70.20973
## 47.85714 115.38325 107.61269
## 48.00000 118.80734 110.65081
## 48.14286 159.04313 150.51809
## 48.28571 122.56970 113.69143
## 48.42857 141.70075 132.48277
## 48.57143 122.29852 111.34372
## 48.71429  86.37880  74.81305
## 48.85714 118.06131 105.91531
## 49.00000 120.54423 107.84447
## 49.14286 154.32203 141.09165
## 49.28571 122.93362 109.19312
## 49.42857 138.80553 124.57316
## 
## $兵庫県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 145.5001 152.4346
## 47.71429 105.3974 112.7618
## 47.85714 144.7411 152.5117
## 48.00000 149.6234 157.7800
## 48.14286 191.2515 199.7765
## 48.28571 156.1126 164.9909
## 48.42857 176.5271 185.7451
## 48.57143 163.6868 174.6416
## 48.71429 130.0752 141.6410
## 48.85714 163.9500 176.0960
## 49.00000 168.5251 181.2249
## 49.14286 204.3076 217.5379
## 49.28571 174.8465 188.5870
## 49.42857 192.5767 206.8091
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 21.50988 21.72586 25.62877 30.33820 29.99467 29.34025 28.79608 25.92241
##  [9] 25.61459 26.18764 30.63517 31.87225 30.70367 29.40780
## 
## $奈良県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 17.17724 14.88369
## 47.71429 17.11188 14.66939
## 47.85714 20.56219 17.88010
## 48.00000 25.23091 22.52727
## 48.14286 24.85947 22.14105
## 48.28571 24.00040 21.17366
## 48.42857 23.27627 20.35426
## 48.57143 20.14975 17.09388
## 48.71429 19.70497 16.57661
## 48.85714 20.03518 16.77826
## 49.00000 24.34852 21.02057
## 49.14286 25.49497 22.11905
## 49.28571 24.18136 20.72866
## 49.42857 22.72325 19.18465
## 
## $奈良県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 25.84251 28.13607
## 47.71429 26.33984 28.78233
## 47.85714 30.69535 33.37744
## 48.00000 35.44550 38.14914
## 48.14286 35.12987 37.84828
## 48.28571 34.68010 37.50684
## 48.42857 34.31589 37.23790
## 48.57143 31.69508 34.75094
## 48.71429 31.52420 34.65257
## 48.85714 32.34010 35.59702
## 49.00000 36.92182 40.24977
## 49.14286 38.24952 41.62545
## 49.28571 37.22598 40.67868
## 49.42857 36.09236 39.63095
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 7.009568 7.000779 7.025504 7.343106 7.254514 6.377013 7.302233 6.954143
##  [9] 7.338323 6.932172 7.023969 7.142553 7.120410 7.455778
## 
## $和歌山県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 4.863012 3.726693
## 47.71429 4.675212 3.444131
## 47.85714 4.605636 3.324635
## 48.00000 4.832477 3.503430
## 48.14286 4.656293 3.280878
## 48.28571 3.694057 2.273786
## 48.42857 4.537139 3.073386
## 48.57143 4.151326 2.667605
## 48.71429 4.475892 2.960613
## 48.85714 4.006855 2.458285
## 49.00000 4.037088 2.455929
## 49.14286 4.095353 2.482262
## 49.28571 4.014061 2.369659
## 49.42857 4.291386 2.616258
## 
## $和歌山県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%      95%
## 47.57143  9.156125 10.29244
## 47.71429  9.326346 10.55743
## 47.85714  9.445371 10.72637
## 48.00000  9.853734 11.18278
## 48.14286  9.852736 11.22815
## 48.28571  9.059968 10.48024
## 48.42857 10.067328 11.53108
## 48.57143  9.756959 11.24068
## 48.71429 10.200753 11.71603
## 48.85714  9.857490 11.40606
## 49.00000 10.010849 11.59201
## 49.14286 10.189754 11.80284
## 49.28571 10.226759 11.87116
## 49.42857 10.620171 12.29530
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.6892077 0.5973984 0.6893077 0.5291504 0.5049679 0.4953682 0.4714039
##  [8] 0.4639202 0.4590251 0.4545214 0.4524095 0.4510041 0.4500110 0.4494520
## 
## $鳥取県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%        95%
## 47.57143 -0.2543151 -0.7537859
## 47.71429 -0.3580588 -0.8638473
## 47.85714 -0.2765052 -0.7877757
## 48.00000 -0.4515203 -0.9706560
## 48.14286 -0.4785235 -0.9991524
## 48.28571 -0.4901261 -1.0118152
## 48.42857 -0.5155821 -1.0380610
## 48.57143 -0.5238697 -1.0467741
## 48.71429 -0.5293988 -1.0526388
## 48.85714 -0.5344242 -1.0579404
## 49.00000 -0.5369721 -1.0607190
## 49.14286 -0.5387753 -1.0627330
## 49.28571 -0.5401412 -1.0642962
## 49.42857 -0.5410562 -1.0653995
## 
## $鳥取県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 1.632731 2.132201
## 47.71429 1.552856 2.058644
## 47.85714 1.655121 2.166391
## 48.00000 1.509821 2.028957
## 48.14286 1.488459 2.009088
## 48.28571 1.480862 2.002552
## 48.42857 1.458390 1.980869
## 48.57143 1.451710 1.974614
## 48.71429 1.447449 1.970689
## 48.85714 1.443467 1.966983
## 49.00000 1.441791 1.965538
## 49.14286 1.440784 1.964741
## 49.28571 1.440163 1.964318
## 49.42857 1.439960 1.964304
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252
##  [8] 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252 0.4693252
## 
## $島根県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 -6.096651 -9.572469
## 47.71429 -6.096651 -9.572469
## 47.85714 -6.096651 -9.572469
## 48.00000 -6.096651 -9.572469
## 48.14286 -6.096651 -9.572469
## 48.28571 -6.096651 -9.572469
## 48.42857 -6.096651 -9.572469
## 48.57143 -6.096651 -9.572469
## 48.71429 -6.096651 -9.572469
## 48.85714 -6.096651 -9.572469
## 49.00000 -6.096651 -9.572469
## 49.14286 -6.096651 -9.572469
## 49.28571 -6.096651 -9.572469
## 49.42857 -6.096651 -9.572469
## 
## $島根県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 7.035301 10.51112
## 47.71429 7.035301 10.51112
## 47.85714 7.035301 10.51112
## 48.00000 7.035301 10.51112
## 48.14286 7.035301 10.51112
## 48.28571 7.035301 10.51112
## 48.42857 7.035301 10.51112
## 48.57143 7.035301 10.51112
## 48.71429 7.035301 10.51112
## 48.85714 7.035301 10.51112
## 49.00000 7.035301 10.51112
## 49.14286 7.035301 10.51112
## 49.28571 7.035301 10.51112
## 49.42857 7.035301 10.51112
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 10.21818  9.93739 10.29899 10.21621 10.21495  9.52318 10.33036 10.29755
##  [9] 10.28915 10.28700 10.28645 10.28631 10.28627 10.28626
## 
## $岡山県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 6.862359 5.085894
## 47.71429 6.312066 4.392936
## 47.85714 6.572210 4.599370
## 48.00000 6.417907 4.407205
## 48.14286 6.352618 4.308023
## 48.28571 5.599378 3.522242
## 48.42857 6.346416 4.237443
## 48.57143 6.173474 3.990320
## 48.71429 6.079804 3.851511
## 48.85714 6.004920 3.738124
## 49.00000 5.935465 3.632193
## 49.14286 5.868145 3.529311
## 49.28571 5.802100 3.428323
## 49.42857 5.737081 3.328890
## 
## $岡山県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 13.57400 15.35047
## 47.71429 13.56271 15.48185
## 47.85714 14.02578 15.99862
## 48.00000 14.01452 16.02522
## 48.14286 14.07728 16.12188
## 48.28571 13.44698 15.52412
## 48.42857 14.31431 16.42328
## 48.57143 14.42163 16.60478
## 48.71429 14.49850 16.72679
## 48.85714 14.56908 16.83587
## 49.00000 14.63743 16.94070
## 49.14286 14.70447 17.04330
## 49.28571 14.77044 17.14422
## 49.42857 14.83544 17.24363
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 38.24644 44.27039 45.16184 44.92171 44.92171 44.92171 44.92171 44.92171
##  [9] 44.92171 44.92171 44.92171 44.92171 44.92171 44.92171
## 
## $広島県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 33.80867 31.45946
## 47.71429 39.09197 36.35068
## 47.85714 39.74880 36.88331
## 48.00000 38.93990 35.77331
## 48.14286 38.22560 34.68088
## 48.28571 37.58047 33.69425
## 48.42857 36.98763 32.78758
## 48.57143 36.43610 31.94409
## 48.71429 35.91830 31.15218
## 48.85714 35.42870 30.40340
## 49.00000 34.96314 29.69139
## 49.14286 34.51840 29.01121
## 49.28571 34.09190 28.35894
## 49.42857 33.68158 27.73141
## 
## $広島県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 42.68421 45.03342
## 47.71429 49.44882 52.19011
## 47.85714 50.57488 53.44038
## 48.00000 50.90353 54.07012
## 48.14286 51.61783 55.16254
## 48.28571 52.26296 56.14918
## 48.42857 52.85580 57.05585
## 48.57143 53.40732 57.89933
## 48.71429 53.92513 58.69124
## 48.85714 54.41473 59.44002
## 49.00000 54.88029 60.15203
## 49.14286 55.32503 60.83221
## 49.28571 55.75153 61.48448
## 49.42857 56.16185 62.11202
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 2.328630 2.292987 2.377068 3.521642 2.339863 2.393341 2.572511 2.445725
##  [9] 2.504033 2.920085 3.021920 2.773912 2.763679 2.653383
## 
## $山口県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%       95%
## 47.57143 -0.2886858 -1.674209
## 47.71429 -0.5336740 -2.030018
## 47.85714 -0.6490536 -2.250986
## 48.00000  0.4042919 -1.245933
## 48.14286 -0.8778209 -2.581160
## 48.28571 -0.8684294 -2.595107
## 48.42857 -0.7444798 -2.500389
## 48.57143 -0.9979613 -2.820939
## 48.71429 -1.0074366 -2.866297
## 48.85714 -0.6291360 -2.507980
## 49.00000 -0.5664679 -2.466045
## 49.14286 -0.8361817 -2.747250
## 49.28571 -0.8708187 -2.794805
## 49.42857 -0.9946648 -2.925825
## 
## $山口県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 4.945946 6.331470
## 47.71429 5.119648 6.615992
## 47.85714 5.403190 7.005122
## 48.00000 6.638992 8.289217
## 48.14286 5.557548 7.260887
## 48.28571 5.655112 7.381789
## 48.42857 5.889503 7.645412
## 48.57143 5.889412 7.712389
## 48.71429 6.015503 7.874363
## 48.85714 6.469305 8.348149
## 49.00000 6.610307 8.509885
## 49.14286 6.384006 8.295074
## 49.28571 6.398176 8.322163
## 49.42857 6.301430 8.232590
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 0.5889710 0.6764720 0.7727278 0.7766616 0.7943428 1.0358820 0.9775153
##  [8] 0.8885719 0.8575582 0.8135027 0.8235570 0.7103432 0.5548674 0.6724391
## 
## $徳島県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%       95%
## 47.57143 -1.0995908 -1.993461
## 47.71429 -1.0962584 -2.034685
## 47.85714 -1.0179784 -1.965921
## 48.00000 -1.0318417 -1.989205
## 48.14286 -1.0317841 -1.998477
## 48.28571 -0.8077001 -1.783633
## 48.42857 -0.8833583 -1.868445
## 48.57143 -1.0621641 -2.094821
## 48.71429 -1.1259852 -2.176010
## 48.85714 -1.1930315 -2.255226
## 49.00000 -1.2057075 -2.279935
## 49.14286 -1.3413999 -2.427527
## 49.28571 -1.5191106 -2.617008
## 49.42857 -1.4235379 -2.533081
## 
## $徳島県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 2.277533 3.171403
## 47.71429 2.449202 3.387629
## 47.85714 2.563434 3.511377
## 48.00000 2.585165 3.542529
## 48.14286 2.620470 3.587163
## 48.28571 2.879464 3.855397
## 48.42857 2.838389 3.823476
## 48.57143 2.839308 3.871965
## 48.71429 2.841101 3.891126
## 48.85714 2.820037 3.882232
## 49.00000 2.852821 3.927049
## 49.14286 2.762086 3.848213
## 49.28571 2.628845 3.726743
## 49.42857 2.768416 3.877959
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 2.795326 2.666319 2.666319 2.666319 2.666319 2.666319 2.666319 2.666319
##  [9] 2.666319 2.666319 2.666319 2.666319 2.666319 2.666319
## 
## $香川県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%        95%
## 47.57143 1.2969380 0.50373913
## 47.71429 1.1272582 0.31252871
## 47.85714 1.1136155 0.29166403
## 48.00000 1.1000916 0.27098108
## 48.14286 1.0866836 0.25047520
## 48.28571 1.0733884 0.23014192
## 48.42857 1.0602032 0.20997695
## 48.57143 1.0471254 0.18997618
## 48.71429 1.0341524 0.17013566
## 48.85714 1.0212817 0.15045160
## 49.00000 1.0085109 0.13092036
## 49.14286 0.9958377 0.11153843
## 49.28571 0.9832600 0.09230244
## 49.42857 0.9707755 0.07320913
## 
## $香川県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 4.293715 5.086914
## 47.71429 4.205379 5.020109
## 47.85714 4.219022 5.040974
## 48.00000 4.232546 5.061657
## 48.14286 4.245954 5.082162
## 48.28571 4.259249 5.102496
## 48.42857 4.272434 5.122661
## 48.57143 4.285512 5.142661
## 48.71429 4.298485 5.162502
## 48.85714 4.311356 5.182186
## 49.00000 4.324127 5.201717
## 49.14286 4.336800 5.221099
## 49.28571 4.349378 5.240335
## 49.42857 4.361862 5.259428
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 4.020384 4.593831 4.317574 4.242262 4.439761 4.242378 5.124925 4.813221
##  [9] 4.813221 4.813221 4.813221 4.813221 4.813221 4.813221
## 
## $愛媛県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                  80%        95%
## 47.57143  1.68342973  0.4463210
## 47.71429  1.83747543  0.3783488
## 47.85714  1.19769559 -0.4538682
## 48.00000  0.79700658 -1.0268014
## 48.14286  0.69731169 -1.2838213
## 48.28571  0.22465887 -1.9021934
## 48.42857  0.84962218 -1.4135864
## 48.57143  0.40001233 -1.9361993
## 48.71429  0.23125509 -2.1942912
## 48.85714  0.06849631 -2.4432093
## 49.00000 -0.08886153 -2.6838674
## 49.14286 -0.24132289 -2.9170369
## 49.28571 -0.38931826 -3.1433763
## 49.42857 -0.53321852 -3.3634527
## 
## $愛媛県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143  6.357338  7.594446
## 47.71429  7.350187  8.809314
## 47.85714  7.437452  9.089016
## 48.00000  7.687517  9.511325
## 48.14286  8.182210 10.163343
## 48.28571  8.260098 10.386950
## 48.42857  9.400228 11.663436
## 48.57143  9.226430 11.562642
## 48.71429  9.395187 11.820734
## 48.85714  9.557946 12.069652
## 49.00000  9.715304 12.310310
## 49.14286  9.867765 12.543479
## 49.28571 10.015761 12.769819
## 49.42857 10.159661 12.989895
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 15.548651 14.384571 12.758295 11.497296 10.319506  9.290736  8.371031
##  [8]  7.555507  6.830296  6.186042  5.613506  5.104768  4.652699  4.250992
## 
## $高知県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%        95%
## 47.57143 13.4668231 12.3647697
## 47.71429 11.9804838 10.7078369
## 47.85714 10.0394528  8.6001845
## 48.00000  8.5770477  7.0311617
## 48.14286  7.2434795  5.6151296
## 48.28571  6.0986955  4.4089314
## 48.42857  5.0898464  3.3528920
## 48.57143  4.2057262  2.4324596
## 48.71429  3.4272970  1.6258580
## 48.85714  2.7416103  0.9182384
## 49.00000  2.1367059  0.2961990
## 49.14286  1.6026210 -0.2513037
## 49.28571  1.1306665 -0.7337848
## 49.42857  0.7133373 -1.1593840
## 
## $高知県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 17.630480 18.732533
## 47.71429 16.788658 18.061305
## 47.85714 15.477138 16.916406
## 48.00000 14.417544 15.963430
## 48.14286 13.395532 15.023881
## 48.28571 12.482776 14.172540
## 48.42857 11.652216 13.389171
## 48.57143 10.905287 12.678554
## 48.71429 10.233295 12.034734
## 48.85714  9.630473 11.453845
## 49.00000  9.090307 10.930814
## 49.14286  8.606915 10.460840
## 49.28571  8.174731 10.039182
## 49.42857  7.788647  9.661368
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 36.52259 28.34256 27.64086 31.58291 34.79546 39.88869 39.50164 35.79395
##  [9] 31.20639 30.06285 31.43953 32.42680 36.01704 36.26955
## 
## $福岡県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%         95%
## 47.57143 22.996573  15.8363315
## 47.71429 12.287700   3.7887727
## 47.85714 10.250250   1.0442174
## 48.00000 13.514297   3.9493516
## 48.14286 16.176740   6.3205869
## 48.28571 20.455063  10.1675239
## 48.42857 18.767789   7.7919540
## 48.57143 12.507989   0.1811466
## 48.71429  6.034628  -7.2904964
## 48.85714  3.633696 -10.3570528
## 49.00000  4.217564 -10.1928759
## 49.14286  4.550808 -10.2058515
## 49.28571  7.340061  -7.8406161
## 49.42857  6.480304  -9.2891715
## 
## $福岡県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 50.04861 57.20885
## 47.71429 44.39741 52.89634
## 47.85714 45.03147 54.23750
## 48.00000 49.65152 59.21647
## 48.14286 53.41417 63.27033
## 48.28571 59.32231 69.60985
## 48.42857 60.23548 71.21132
## 48.57143 59.07991 71.40675
## 48.71429 56.37815 69.70327
## 48.85714 56.49200 70.48275
## 49.00000 58.66150 73.07194
## 49.14286 60.30280 75.05946
## 49.28571 64.69402 79.87470
## 49.42857 66.05880 81.82828
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 3.981013 3.981013 3.981013 3.981013 3.981013 3.981013 3.981013 3.981013
##  [9] 3.981013 3.981013 3.981013 3.981013 3.981013 3.981013
## 
## $佐賀県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%         95%
## 47.57143 1.841342  0.70866921
## 47.71429 1.771027  0.60113094
## 47.85714 1.702881  0.49691030
## 48.00000 1.636715  0.39571797
## 48.14286 1.572365  0.29730437
## 48.28571 1.509691  0.20145242
## 48.42857 1.448568  0.10797195
## 48.57143 1.388885  0.01669517
## 48.71429 1.330546 -0.07252678
## 48.85714 1.273464 -0.15982672
## 49.00000 1.217560 -0.24532376
## 49.14286 1.162765 -0.32912520
## 49.28571 1.109016 -0.41132808
## 49.42857 1.056254 -0.49202053
## 
## $佐賀県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 6.120684 7.253357
## 47.71429 6.190999 7.360896
## 47.85714 6.259146 7.465116
## 48.00000 6.325312 7.566309
## 48.14286 6.389661 7.664722
## 48.28571 6.452335 7.760574
## 48.42857 6.513459 7.854055
## 48.57143 6.573141 7.945331
## 48.71429 6.631481 8.034553
## 48.85714 6.688563 8.121853
## 49.00000 6.744466 8.207350
## 49.14286 6.799261 8.291152
## 49.28571 6.853011 8.373355
## 49.42857 6.905773 8.454047
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] -0.2232267 -0.3663582 -0.1358717 -0.4535299 -0.4729121 -0.2455608
##  [7] -0.6539893 -0.4183948 -0.6350609 -0.4032687 -0.6555523 -0.6593986
## [13] -0.7473297 -0.6608566
## 
## $長崎県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%       95%
## 47.57143 -2.319861 -3.429752
## 47.71429 -2.691452 -3.922282
## 47.85714 -2.943281 -4.429433
## 48.00000 -3.537936 -5.170722
## 48.14286 -3.873333 -5.673407
## 48.28571 -3.904658 -5.841667
## 48.42857 -4.569306 -6.641949
## 48.57143 -4.709519 -6.981102
## 48.71429 -5.203392 -7.621720
## 48.85714 -5.265743 -7.839781
## 49.00000 -5.778992 -8.491177
## 49.14286 -6.039216 -8.887119
## 49.28571 -6.367815 -9.343121
## 49.42857 -6.514134 -9.612672
## 
## $長崎県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 1.873408 2.983299
## 47.71429 1.958735 3.189566
## 47.85714 2.671537 4.157690
## 48.00000 2.630876 4.263662
## 48.14286 2.927508 4.727582
## 48.28571 3.413536 5.350545
## 48.42857 3.261327 5.333970
## 48.57143 3.872729 6.144312
## 48.71429 3.933270 6.351598
## 48.85714 4.459205 7.033244
## 49.00000 4.467887 7.180072
## 49.14286 4.720419 7.568322
## 49.28571 4.873156 7.848461
## 49.42857 5.192421 8.290959
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 8.191823 9.864909 9.042280 8.859713 8.693168 8.541236 8.402638 8.276201
##  [9] 8.160860 8.055640 7.959653 7.872090 7.792210 7.719340
## 
## $熊本県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                80%        95%
## 47.57143 3.7436769  1.3889705
## 47.71429 4.2695278  1.3075116
## 47.85714 3.3623395  0.3555608
## 48.00000 2.9000184 -0.2548535
## 48.14286 2.5000909 -0.7783259
## 48.28571 2.1511998 -1.2314813
## 48.42857 1.8447784 -1.6267426
## 48.57143 1.5741598 -1.9736867
## 48.71429 1.3340320 -2.2798724
## 48.85714 1.1200847 -2.5513765
## 49.00000 0.9287692 -2.7931560
## 49.14286 0.7571289 -3.0093038
## 49.28571 0.6026767 -3.2032323
## 49.42857 0.4633028 -3.3778111
## 
## $熊本県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 12.63997 14.99468
## 47.71429 15.46029 18.42231
## 47.85714 14.72222 17.72900
## 48.00000 14.81941 17.97428
## 48.14286 14.88624 18.16466
## 48.28571 14.93127 18.31395
## 48.42857 14.96050 18.43202
## 48.57143 14.97824 18.52609
## 48.71429 14.98769 18.60159
## 48.85714 14.99120 18.66266
## 49.00000 14.99054 18.71246
## 49.14286 14.98705 18.75348
## 49.28571 14.98174 18.78765
## 49.42857 14.97538 18.81649
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 19.12422 18.43646 19.03183 19.11452 19.55820 20.37781 20.93560 21.92918
##  [9] 22.53425 23.15984 23.74139 24.19013 24.61474 25.01652
## 
## $大分県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 17.23755 16.23880
## 47.71429 16.34696 15.24085
## 47.85714 16.56740 15.26280
## 48.00000 16.45256 15.04341
## 48.14286 16.83926 15.39994
## 48.28571 17.57743 16.09500
## 48.42857 18.02837 16.48937
## 48.57143 18.98228 17.42229
## 48.71429 19.48270 17.86731
## 48.85714 20.00556 18.33579
## 49.00000 20.44679 18.70273
## 49.14286 20.70651 18.86239
## 49.28571 20.93470 18.98660
## 49.42857 21.13394 19.07863
## 
## $大分県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 21.01090 22.00964
## 47.71429 20.52595 21.63206
## 47.85714 21.49627 22.80086
## 48.00000 21.77648 23.18564
## 48.14286 22.27714 23.71646
## 48.28571 23.17820 24.66063
## 48.42857 23.84284 25.38184
## 48.57143 24.87608 26.43608
## 48.71429 25.58580 27.20119
## 48.85714 26.31412 27.98390
## 49.00000 27.03600 28.78006
## 49.14286 27.67375 29.51787
## 49.28571 28.29479 30.24288
## 49.42857 28.89910 30.95441
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 8.205078 8.181516 8.181516 8.181516 8.181516 8.181516 8.181516 8.181516
##  [9] 8.181516 8.181516 8.181516 8.181516 8.181516 8.181516
## 
## $宮崎県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%        95%
## 47.57143 5.188408  3.5914789
## 47.71429 5.013458  3.3363901
## 47.85714 4.689774  2.8413572
## 48.00000 4.393649  2.3884730
## 48.14286 4.119052  1.9685136
## 48.28571 3.861876  1.5751971
## 48.42857 3.619174  1.2040164
## 48.57143 3.388747  0.8516079
## 48.71429 3.168901  0.5153825
## 48.85714 2.958300  0.1932963
## 49.00000 2.755867 -0.1162973
## 49.14286 2.560721 -0.4147481
## 49.28571 2.372126 -0.7031792
## 49.42857 2.189464 -0.9825366
## 
## $宮崎県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 11.22175 12.81868
## 47.71429 11.34957 13.02664
## 47.85714 11.67326 13.52167
## 48.00000 11.96938 13.97456
## 48.14286 12.24398 14.39452
## 48.28571 12.50116 14.78783
## 48.42857 12.74386 15.15902
## 48.57143 12.97428 15.51142
## 48.71429 13.19413 15.84765
## 48.85714 13.40473 16.16974
## 49.00000 13.60716 16.47933
## 49.14286 13.80231 16.77778
## 49.28571 13.99091 17.06621
## 49.42857 14.17357 17.34557
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 5.298879 4.901519 4.676315 4.548681 4.476345 4.435348 4.412114 4.398945
##  [9] 4.391482 4.387252 4.384855 4.383497 4.382727 4.382290
## 
## $鹿児島県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##                 80%       95%
## 47.57143  1.2156255 -0.945918
## 47.71429  0.1330903 -2.391163
## 47.85714 -0.3359385 -2.989265
## 48.00000 -0.5670538 -3.275160
## 48.14286 -0.6910344 -3.426480
## 48.28571 -0.7624533 -3.514003
## 48.42857 -0.8066059 -3.569229
## 48.57143 -0.8360974 -3.607362
## 48.71429 -0.8575264 -3.636184
## 48.85714 -0.8744589 -3.659841
## 49.00000 -0.8888601 -3.680596
## 49.14286 -0.9018255 -3.699706
## 49.28571 -0.9139702 -3.717872
## 49.42857 -0.9256409 -3.735490
## 
## $鹿児島県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 9.382131 11.54368
## 47.71429 9.669947 12.19420
## 47.85714 9.688569 12.34190
## 48.00000 9.664416 12.37252
## 48.14286 9.643724 12.37917
## 48.28571 9.633150 12.38470
## 48.42857 9.630833 12.39346
## 48.57143 9.633988 12.40525
## 48.71429 9.640491 12.41915
## 48.85714 9.648964 12.43435
## 49.00000 9.658571 12.45031
## 49.14286 9.668819 12.46670
## 49.28571 9.679423 12.48333
## 49.42857 9.690221 12.50007
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##  [1] 41.64505 38.59408 37.07193 42.17709 39.40405 41.19928 36.47275 38.88102
##  [9] 38.88102 38.88102 38.88102 38.88102 38.88102 38.88102
## 
## $沖縄県$lower
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%        95%
## 47.57143 29.08432 22.4350774
## 47.71429 25.02668 17.8445269
## 47.85714 22.56755 14.8894001
## 48.00000 26.79270 18.6487012
## 48.14286 23.18734 14.6027248
## 48.28571 24.19091 15.1872308
## 48.42857 18.70799  9.3038891
## 48.57143 19.10160  8.6310111
## 48.71429 18.03406  6.9983528
## 48.85714 17.01859  5.4453221
## 49.00000 16.04824  3.9612932
## 49.14286 15.11747  2.5378123
## 49.28571 14.22182  1.1680226
## 49.42857 13.35757 -0.1537287
## 
## $沖縄県$upper
## Time Series:
## Start = c(47, 5) 
## End = c(49, 4) 
## Frequency = 7 
##               80%      95%
## 47.57143 54.20578 60.85503
## 47.71429 52.16148 59.34362
## 47.85714 51.57630 59.25445
## 48.00000 57.56148 65.70548
## 48.14286 55.62077 64.20538
## 48.28571 58.20764 67.21132
## 48.42857 54.23752 63.64162
## 48.57143 58.66043 69.13102
## 48.71429 59.72797 70.76368
## 48.85714 60.74344 72.31671
## 49.00000 61.71380 73.80074
## 49.14286 62.64456 75.22422
## 49.28571 63.54022 76.59401
## 49.42857 64.40447 77.91576